Overview

Dataset statistics

Number of variables22
Number of observations28200
Missing cells22614
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 MiB
Average record size in memory411.4 B

Variable types

Categorical4
Numeric18

Warnings

tracking_id has a high cardinality: 28200 distinct values High cardinality
datetime has a high cardinality: 28200 distinct values High cardinality
motor_torque(N-m) is highly correlated with generator_temperature(°C)High correlation
generator_temperature(°C) is highly correlated with motor_torque(N-m)High correlation
atmospheric_temperature(°C) has 3450 (12.2%) missing values Missing
atmospheric_pressure(Pascal) has 2707 (9.6%) missing values Missing
windmill_body_temperature(°C) has 2363 (8.4%) missing values Missing
wind_direction(°) has 5103 (18.1%) missing values Missing
rotor_torque(N-m) has 572 (2.0%) missing values Missing
turbine_status has 1759 (6.2%) missing values Missing
blade_length(m) has 5093 (18.1%) missing values Missing
windmill_height(m) has 543 (1.9%) missing values Missing
tracking_id is uniformly distributed Uniform
datetime is uniformly distributed Uniform
tracking_id has unique values Unique
datetime has unique values Unique
blade_breadth(m) has unique values Unique

Reproduction

Analysis started2021-05-26 15:21:29.924854
Analysis finished2021-05-26 15:22:26.263920
Duration56.34 seconds
Software versionpandas-profiling v2.12.0
Download configurationconfig.yaml

Variables

tracking_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct28200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
WM_38470
 
1
WM_1082
 
1
WM_30836
 
1
WM_36956
 
1
WM_35063
 
1
Other values (28195)
28195 

Length

Max length8
Median length8
Mean length7.725177305
Min length4

Characters and Unicode

Total characters217850
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28200 ?
Unique (%)100.0%

Sample

1st rowWM_33725
2nd rowWM_698
3rd rowWM_39146
4th rowWM_6757
5th rowWM_21521
ValueCountFrequency (%)
WM_384701
 
< 0.1%
WM_10821
 
< 0.1%
WM_308361
 
< 0.1%
WM_369561
 
< 0.1%
WM_350631
 
< 0.1%
WM_18181
 
< 0.1%
WM_330901
 
< 0.1%
WM_258061
 
< 0.1%
WM_135161
 
< 0.1%
WM_1991
 
< 0.1%
Other values (28190)28190
> 99.9%
2021-05-27T04:22:26.602919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wm_68881
 
< 0.1%
wm_234421
 
< 0.1%
wm_301381
 
< 0.1%
wm_52011
 
< 0.1%
wm_209831
 
< 0.1%
wm_222881
 
< 0.1%
wm_150251
 
< 0.1%
wm_175701
 
< 0.1%
wm_322391
 
< 0.1%
wm_186391
 
< 0.1%
Other values (28190)28190
> 99.9%

Most occurring characters

ValueCountFrequency (%)
W28200
12.9%
M28200
12.9%
_28200
12.9%
218422
8.5%
118347
8.4%
318230
8.4%
411468
 
5.3%
711270
 
5.2%
511249
 
5.2%
911249
 
5.2%
Other values (3)33015
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number133250
61.2%
Uppercase Letter56400
25.9%
Connector Punctuation28200
 
12.9%

Most frequent character per category

ValueCountFrequency (%)
218422
13.8%
118347
13.8%
318230
13.7%
411468
8.6%
711270
8.5%
511249
8.4%
911249
8.4%
811238
8.4%
611120
8.3%
010657
8.0%
ValueCountFrequency (%)
W28200
50.0%
M28200
50.0%
ValueCountFrequency (%)
_28200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common161450
74.1%
Latin56400
 
25.9%

Most frequent character per script

ValueCountFrequency (%)
_28200
17.5%
218422
11.4%
118347
11.4%
318230
11.3%
411468
7.1%
711270
 
7.0%
511249
 
7.0%
911249
 
7.0%
811238
 
7.0%
611120
 
6.9%
ValueCountFrequency (%)
W28200
50.0%
M28200
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII217850
100.0%

Most frequent character per block

ValueCountFrequency (%)
W28200
12.9%
M28200
12.9%
_28200
12.9%
218422
8.5%
118347
8.4%
318230
8.4%
411468
 
5.3%
711270
 
5.2%
511249
 
5.2%
911249
 
5.2%
Other values (3)33015
15.2%

datetime
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct28200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2019-09-07 08:33:20
 
1
2019-06-14 01:03:20
 
1
2019-01-19 21:13:20
 
1
2019-01-07 17:33:20
 
1
2019-05-09 02:33:20
 
1
Other values (28195)
28195 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters535800
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28200 ?
Unique (%)100.0%

Sample

1st row2019-08-04 14:33:20
2nd row2018-11-05 10:13:20
3rd row2019-09-14 14:03:20
4th row2018-12-25 15:33:20
5th row2019-05-04 03:13:20
ValueCountFrequency (%)
2019-09-07 08:33:201
 
< 0.1%
2019-06-14 01:03:201
 
< 0.1%
2019-01-19 21:13:201
 
< 0.1%
2019-01-07 17:33:201
 
< 0.1%
2019-05-09 02:33:201
 
< 0.1%
2019-01-29 23:43:201
 
< 0.1%
2019-02-09 18:33:201
 
< 0.1%
2019-05-28 10:33:201
 
< 0.1%
2018-12-25 09:23:201
 
< 0.1%
2018-12-24 05:43:201
 
< 0.1%
Other values (28190)28190
> 99.9%
2021-05-27T04:22:26.969410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16:43:20221
 
0.4%
18:33:20218
 
0.4%
19:33:20214
 
0.4%
20:23:20213
 
0.4%
18:53:20213
 
0.4%
05:43:20213
 
0.4%
17:33:20212
 
0.4%
17:03:20211
 
0.4%
19:23:20210
 
0.4%
19:03:20209
 
0.4%
Other values (455)54266
96.2%

Most occurring characters

ValueCountFrequency (%)
0110589
20.6%
286401
16.1%
171444
13.3%
-56400
10.5%
:56400
10.5%
342744
 
8.0%
930067
 
5.6%
28200
 
5.3%
813375
 
2.5%
512785
 
2.4%
Other values (3)27395
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number394800
73.7%
Dash Punctuation56400
 
10.5%
Other Punctuation56400
 
10.5%
Space Separator28200
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
0110589
28.0%
286401
21.9%
171444
18.1%
342744
 
10.8%
930067
 
7.6%
813375
 
3.4%
512785
 
3.2%
411435
 
2.9%
78000
 
2.0%
67960
 
2.0%
ValueCountFrequency (%)
-56400
100.0%
ValueCountFrequency (%)
28200
100.0%
ValueCountFrequency (%)
:56400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common535800
100.0%

Most frequent character per script

ValueCountFrequency (%)
0110589
20.6%
286401
16.1%
171444
13.3%
-56400
10.5%
:56400
10.5%
342744
 
8.0%
930067
 
5.6%
28200
 
5.3%
813375
 
2.5%
512785
 
2.4%
Other values (3)27395
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII535800
100.0%

Most frequent character per block

ValueCountFrequency (%)
0110589
20.6%
286401
16.1%
171444
13.3%
-56400
10.5%
:56400
10.5%
342744
 
8.0%
930067
 
5.6%
28200
 
5.3%
813375
 
2.5%
512785
 
2.4%
Other values (3)27395
 
5.1%

wind_speed(m/s)
Real number (ℝ)

Distinct27727
Distinct (%)99.3%
Missing273
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean69.03707139
Minimum-496.2110289
Maximum601.4556704
Zeros0
Zeros (%)0.0%
Negative2142
Negative (%)7.6%
Memory size440.6 KiB
2021-05-27T04:22:27.117409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-496.2110289
5-th percentile-65.75486742
Q120.88350151
median93.30212921
Q395.26805784
95-th percentile212.0745349
Maximum601.4556704
Range1097.666699
Interquartile range (IQR)74.38455633

Descriptive statistics

Standard deviation76.27564488
Coefficient of variation (CV)1.10485053
Kurtosis4.11259989
Mean69.03707139
Median Absolute Deviation (MAD)3.552179364
Skewness-0.06047716509
Sum1927998.293
Variance5817.974002
MonotonicityNot monotonic
2021-05-27T04:22:27.272553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10193
 
0.7%
81.818181829
 
< 0.1%
93.611028091
 
< 0.1%
94.226194471
 
< 0.1%
95.472676961
 
< 0.1%
93.027332761
 
< 0.1%
92.743993941
 
< 0.1%
92.943330751
 
< 0.1%
93.988862281
 
< 0.1%
17.6301661
 
< 0.1%
Other values (27717)27717
98.3%
(Missing)273
 
1.0%
ValueCountFrequency (%)
-496.21102891
< 0.1%
-402.60873621
< 0.1%
-391.20853041
< 0.1%
-354.00646131
< 0.1%
-330.58313151
< 0.1%
-329.45384951
< 0.1%
-328.41655881
< 0.1%
-323.43046271
< 0.1%
-321.42999071
< 0.1%
-318.08109381
< 0.1%
ValueCountFrequency (%)
601.45567041
< 0.1%
513.0786241
< 0.1%
498.87752841
< 0.1%
488.09596451
< 0.1%
484.65646291
< 0.1%
481.80321521
< 0.1%
480.26104671
< 0.1%
479.57561121
< 0.1%
479.28110841
< 0.1%
473.73791511
< 0.1%

atmospheric_temperature(°C)
Real number (ℝ)

MISSING

Distinct20809
Distinct (%)84.1%
Missing3450
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean0.3837270705
Minimum-99
Maximum80.21744352
Zeros0
Zeros (%)0.0%
Negative4199
Negative (%)14.9%
Memory size440.6 KiB
2021-05-27T04:22:27.421554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q17.948900131
median16.10241035
Q323.68728525
95-th percentile35.77114497
Maximum80.21744352
Range179.2174435
Interquartile range (IQR)15.73838512

Descriptive statistics

Standard deviation44.27853354
Coefficient of variation (CV)115.3906955
Kurtosis1.137602533
Mean0.3837270705
Median Absolute Deviation (MAD)7.85615852
Skewness-1.674895242
Sum9497.244995
Variance1960.588533
MonotonicityNot monotonic
2021-05-27T04:22:27.568583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-993942
 
14.0%
19.429081561
 
< 0.1%
11.525959631
 
< 0.1%
11.60435481
 
< 0.1%
29.164471411
 
< 0.1%
10.913344581
 
< 0.1%
35.154762161
 
< 0.1%
25.511930141
 
< 0.1%
9.6091623691
 
< 0.1%
12.963669621
 
< 0.1%
Other values (20799)20799
73.8%
(Missing)3450
 
12.2%
ValueCountFrequency (%)
-993942
14.0%
-53.795813081
 
< 0.1%
-33.113770251
 
< 0.1%
-28.445567481
 
< 0.1%
-27.484168761
 
< 0.1%
-26.623474651
 
< 0.1%
-26.221296421
 
< 0.1%
-25.910067981
 
< 0.1%
-25.82862671
 
< 0.1%
-24.840696731
 
< 0.1%
ValueCountFrequency (%)
80.217443521
< 0.1%
76.902656971
< 0.1%
74.765363321
< 0.1%
72.449450851
< 0.1%
72.12026951
< 0.1%
72.03264931
< 0.1%
69.633727961
< 0.1%
69.319338991
< 0.1%
68.948925751
< 0.1%
68.780967351
< 0.1%

shaft_temperature(°C)
Real number (ℝ)

Distinct27625
Distinct (%)98.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.08538671
Minimum-99
Maximum169.8204551
Zeros0
Zeros (%)0.0%
Negative1417
Negative (%)5.0%
Memory size440.6 KiB
2021-05-27T04:22:27.729554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-0.1621536448
Q141.63323757
median43.68608193
Q345.67368539
95-th percentile77.86079602
Maximum169.8204551
Range268.8204551
Interquartile range (IQR)4.040447816

Descriptive statistics

Standard deviation27.20442954
Coefficient of variation (CV)0.6786620205
Kurtosis12.24662229
Mean40.08538671
Median Absolute Deviation (MAD)2.019726623
Skewness-2.525168271
Sum1130327.735
Variance740.0809866
MonotonicityNot monotonic
2021-05-27T04:22:27.880281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99557
 
2.0%
3010
 
< 0.1%
-59
 
< 0.1%
42.242690441
 
< 0.1%
-5.6917387411
 
< 0.1%
4.0872618841
 
< 0.1%
43.701011291
 
< 0.1%
44.166945891
 
< 0.1%
13.99928721
 
< 0.1%
93.638921551
 
< 0.1%
Other values (27615)27615
97.9%
(Missing)2
 
< 0.1%
ValueCountFrequency (%)
-99557
2.0%
-93.086235561
 
< 0.1%
-76.268091091
 
< 0.1%
-75.87023131
 
< 0.1%
-73.713284691
 
< 0.1%
-69.171414821
 
< 0.1%
-67.186253041
 
< 0.1%
-65.820695311
 
< 0.1%
-65.038874451
 
< 0.1%
-64.172710231
 
< 0.1%
ValueCountFrequency (%)
169.82045511
< 0.1%
166.44060541
< 0.1%
155.30445141
< 0.1%
151.01669521
< 0.1%
149.69117811
< 0.1%
144.23453661
< 0.1%
143.48323181
< 0.1%
142.83182941
< 0.1%
142.4238091
< 0.1%
139.37366241
< 0.1%

blades_angle(°)
Real number (ℝ)

Distinct22830
Distinct (%)81.6%
Missing216
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean-9.654038032
Minimum-146.2595427
Maximum165.9321232
Zeros5
Zeros (%)< 0.1%
Negative15475
Negative (%)54.9%
Memory size440.6 KiB
2021-05-27T04:22:28.029266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-146.2595427
5-th percentile-99
Q1-1.197651599
median-0.4956079048
Q35.495030481
95-th percentile65.98435133
Maximum165.9321232
Range312.1916659
Interquartile range (IQR)6.69268208

Descriptive statistics

Standard deviation47.91816146
Coefficient of variation (CV)-4.963535601
Kurtosis0.298796478
Mean-9.654038032
Median Absolute Deviation (MAD)2.388084697
Skewness-0.6521221851
Sum-270158.6003
Variance2296.150198
MonotonicityNot monotonic
2021-05-27T04:22:28.163265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-995151
 
18.3%
05
 
< 0.1%
-66.250274861
 
< 0.1%
10.108202491
 
< 0.1%
4.6166506921
 
< 0.1%
37.108811531
 
< 0.1%
-1.1757948031
 
< 0.1%
0.31089420061
 
< 0.1%
38.780261771
 
< 0.1%
-1.2767761781
 
< 0.1%
Other values (22820)22820
80.9%
(Missing)216
 
0.8%
ValueCountFrequency (%)
-146.25954271
 
< 0.1%
-137.65904441
 
< 0.1%
-995151
18.3%
-74.78973591
 
< 0.1%
-74.708260341
 
< 0.1%
-74.405341091
 
< 0.1%
-74.380495521
 
< 0.1%
-74.342723721
 
< 0.1%
-74.311469171
 
< 0.1%
-74.288817051
 
< 0.1%
ValueCountFrequency (%)
165.93212321
< 0.1%
165.16936721
< 0.1%
164.51918541
< 0.1%
162.20451281
< 0.1%
161.71784011
< 0.1%
160.95995661
< 0.1%
157.58355641
< 0.1%
147.84615281
< 0.1%
143.16744511
< 0.1%
141.89236031
< 0.1%

gearbox_temperature(°C)
Real number (ℝ)

Distinct27911
Distinct (%)99.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean41.02775488
Minimum-244.9740978
Maximum999
Zeros0
Zeros (%)0.0%
Negative3091
Negative (%)11.0%
Memory size440.6 KiB
2021-05-27T04:22:28.314265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-244.9740978
5-th percentile-40.9669709
Q140.5579519
median43.22173481
Q345.87942476
95-th percentile117.7077406
Maximum999
Range1243.974098
Interquartile range (IQR)5.321472857

Descriptive statistics

Standard deviation43.6636055
Coefficient of variation (CV)1.064245548
Kurtosis28.45115798
Mean41.02775488
Median Absolute Deviation (MAD)2.661889883
Skewness0.8868464413
Sum1156941.66
Variance1906.510445
MonotonicityNot monotonic
2021-05-27T04:22:28.466265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99272
 
1.0%
-59
 
< 0.1%
308
 
< 0.1%
9993
 
< 0.1%
44.017175691
 
< 0.1%
90.647553471
 
< 0.1%
43.568325791
 
< 0.1%
40.969175141
 
< 0.1%
15.024475621
 
< 0.1%
115.44029171
 
< 0.1%
Other values (27901)27901
98.9%
ValueCountFrequency (%)
-244.97409781
< 0.1%
-201.1654651
< 0.1%
-194.63793411
< 0.1%
-189.423951
< 0.1%
-188.76347261
< 0.1%
-188.48272251
< 0.1%
-186.89656651
< 0.1%
-186.05176571
< 0.1%
-184.38317671
< 0.1%
-179.18221481
< 0.1%
ValueCountFrequency (%)
9993
< 0.1%
348.68654211
 
< 0.1%
286.01933381
 
< 0.1%
276.25253361
 
< 0.1%
274.31282911
 
< 0.1%
273.20175491
 
< 0.1%
272.88104471
 
< 0.1%
272.25574841
 
< 0.1%
271.1664431
 
< 0.1%
271.01026411
 
< 0.1%

engine_temperature(°C)
Real number (ℝ≥0)

Distinct28188
Distinct (%)100.0%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean42.61423856
Minimum3.167151021
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size440.6 KiB
2021-05-27T04:22:28.616265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.167151021
5-th percentile39.06765114
Q141.9113647
median43.52529653
Q345.17424574
95-th percentile47.68384432
Maximum50
Range46.83284898
Interquartile range (IQR)3.262881042

Descriptive statistics

Standard deviation6.124545785
Coefficient of variation (CV)0.1437206434
Kurtosis16.76641786
Mean42.61423856
Median Absolute Deviation (MAD)1.630260113
Skewness-3.944775609
Sum1201210.157
Variance37.51006107
MonotonicityNot monotonic
2021-05-27T04:22:28.762268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.120340791
 
< 0.1%
46.644358721
 
< 0.1%
44.219219481
 
< 0.1%
45.560835911
 
< 0.1%
48.24880721
 
< 0.1%
47.643548121
 
< 0.1%
42.948182091
 
< 0.1%
46.118913381
 
< 0.1%
43.61893171
 
< 0.1%
43.564247891
 
< 0.1%
Other values (28178)28178
99.9%
(Missing)12
 
< 0.1%
ValueCountFrequency (%)
3.1671510211
< 0.1%
3.2829938961
< 0.1%
3.4725128091
< 0.1%
3.5374310341
< 0.1%
3.5680554391
< 0.1%
3.7789112851
< 0.1%
3.7915115091
< 0.1%
4.1050500441
< 0.1%
4.1380983161
< 0.1%
4.2032348491
< 0.1%
ValueCountFrequency (%)
501
< 0.1%
49.945700211
< 0.1%
49.844299241
< 0.1%
49.836065721
< 0.1%
49.826796571
< 0.1%
49.817551421
< 0.1%
49.815815121
< 0.1%
49.812471261
< 0.1%
49.741449951
< 0.1%
49.739216891
< 0.1%

motor_torque(N-m)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27660
Distinct (%)98.2%
Missing24
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1710.819803
Minimum500
Maximum3000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size440.6 KiB
2021-05-27T04:22:28.908364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile617.5541411
Q1870.3402385
median2031.84954
Q32462.585729
95-th percentile2886.182951
Maximum3000
Range2500
Interquartile range (IQR)1592.24549

Descriptive statistics

Standard deviation827.2055367
Coefficient of variation (CV)0.483514123
Kurtosis-1.612981589
Mean1710.819803
Median Absolute Deviation (MAD)843.2315225
Skewness0.03425751523
Sum48204058.77
Variance684269
MonotonicityNot monotonic
2021-05-27T04:22:29.057365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500306
 
1.1%
1001212
 
0.8%
2093.4383121
 
< 0.1%
540.22854281
 
< 0.1%
2096.801191
 
< 0.1%
761.32302271
 
< 0.1%
2164.0628921
 
< 0.1%
2684.4182561
 
< 0.1%
876.55275671
 
< 0.1%
2746.1566581
 
< 0.1%
Other values (27650)27650
98.0%
(Missing)24
 
0.1%
ValueCountFrequency (%)
500306
1.1%
500.09081011
 
< 0.1%
500.109391
 
< 0.1%
500.12365271
 
< 0.1%
500.12598911
 
< 0.1%
500.13245331
 
< 0.1%
500.15479371
 
< 0.1%
500.16401791
 
< 0.1%
500.1757441
 
< 0.1%
500.18235961
 
< 0.1%
ValueCountFrequency (%)
30001
< 0.1%
2992.7373321
< 0.1%
2992.3843281
< 0.1%
2990.5383071
< 0.1%
2989.1884151
< 0.1%
2987.7458461
< 0.1%
2987.7272521
< 0.1%
2987.588971
< 0.1%
2987.1267981
< 0.1%
2985.6640171
< 0.1%

generator_temperature(°C)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28187
Distinct (%)> 99.9%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean65.02785702
Minimum33.89377879
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size440.6 KiB
2021-05-27T04:22:29.214362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum33.89377879
5-th percentile37.40261984
Q141.19850943
median70.72953276
Q378.94584865
95-th percentile93.91805674
Maximum100
Range66.10622121
Interquartile range (IQR)37.74733922

Descriptive statistics

Standard deviation19.81649949
Coefficient of variation (CV)0.3047386213
Kurtosis-1.369991071
Mean65.02785702
Median Absolute Deviation (MAD)17.26340135
Skewness-0.1908094021
Sum1833005.234
Variance392.6936522
MonotonicityNot monotonic
2021-05-27T04:22:29.353651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502
 
< 0.1%
95.329279931
 
< 0.1%
76.282443851
 
< 0.1%
94.502965431
 
< 0.1%
36.825743351
 
< 0.1%
84.363109741
 
< 0.1%
38.869455291
 
< 0.1%
37.948021141
 
< 0.1%
40.51367661
 
< 0.1%
37.10024341
 
< 0.1%
Other values (28177)28177
99.9%
(Missing)12
 
< 0.1%
ValueCountFrequency (%)
33.893778791
< 0.1%
33.922899251
< 0.1%
33.924943551
< 0.1%
33.927954891
< 0.1%
33.936889091
< 0.1%
33.94626321
< 0.1%
33.952818031
< 0.1%
33.959611
< 0.1%
34.006206171
< 0.1%
34.012136451
< 0.1%
ValueCountFrequency (%)
1001
< 0.1%
99.734940761
< 0.1%
99.520806021
< 0.1%
99.514504531
< 0.1%
99.336596991
< 0.1%
99.222361551
< 0.1%
99.171200511
< 0.1%
99.100445061
< 0.1%
99.053747511
< 0.1%
99.029636631
< 0.1%

atmospheric_pressure(Pascal)
Real number (ℝ)

MISSING

Distinct25492
Distinct (%)> 99.9%
Missing2707
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean53185.06488
Minimum-1188624.131
Maximum1272551.895
Zeros0
Zeros (%)0.0%
Negative3282
Negative (%)11.6%
Memory size440.6 KiB
2021-05-27T04:22:29.514652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1188624.131
5-th percentile-291773.9606
Q116794.92149
median18191.12587
Q3118113.2898
95-th percentile396465.2987
Maximum1272551.895
Range2461176.027
Interquartile range (IQR)101318.3683

Descriptive statistics

Standard deviation187503.6156
Coefficient of variation (CV)3.525493784
Kurtosis4.479877501
Mean53185.06488
Median Absolute Deviation (MAD)89036.04864
Skewness0.05667093205
Sum1355846859
Variance3.515760588 × 1010
MonotonicityNot monotonic
2021-05-27T04:22:29.674651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150002
 
< 0.1%
16925.803111
 
< 0.1%
17903.772111
 
< 0.1%
175905.05461
 
< 0.1%
-66607.187141
 
< 0.1%
129081.06661
 
< 0.1%
17321.33141
 
< 0.1%
18544.177011
 
< 0.1%
116548.4821
 
< 0.1%
16621.88341
 
< 0.1%
Other values (25482)25482
90.4%
(Missing)2707
 
9.6%
ValueCountFrequency (%)
-1188624.1311
< 0.1%
-1021641.5991
< 0.1%
-1019257.1641
< 0.1%
-1010616.3081
< 0.1%
-1010037.081
< 0.1%
-962090.55991
< 0.1%
-952409.56231
< 0.1%
-925996.20621
< 0.1%
-916586.00771
< 0.1%
-914153.88171
< 0.1%
ValueCountFrequency (%)
1272551.8951
< 0.1%
1265097.6921
< 0.1%
1205423.991
< 0.1%
1187983.5331
< 0.1%
1182842.7321
< 0.1%
1135375.7161
< 0.1%
1123587.9111
< 0.1%
1114198.1721
< 0.1%
1105042.3991
< 0.1%
1093598.1651
< 0.1%

area_temperature(°C)
Real number (ℝ)

Distinct28170
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.735091
Minimum-30
Maximum55
Zeros0
Zeros (%)0.0%
Negative39
Negative (%)0.1%
Memory size440.6 KiB
2021-05-27T04:22:29.833659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-30
5-th percentile21.53422511
Q127.31164381
median32.60519501
Q338.23238673
95-th percentile45.08603044
Maximum55
Range85
Interquartile range (IQR)10.92074292

Descriptive statistics

Standard deviation7.70339096
Coefficient of variation (CV)0.2353251732
Kurtosis4.436514479
Mean32.735091
Median Absolute Deviation (MAD)5.449118132
Skewness-0.6254243112
Sum923129.5663
Variance59.34223228
MonotonicityNot monotonic
2021-05-27T04:22:29.958324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3031
 
0.1%
30.334296231
 
< 0.1%
18.600740451
 
< 0.1%
38.420260361
 
< 0.1%
28.073637561
 
< 0.1%
45.320134571
 
< 0.1%
41.751341721
 
< 0.1%
37.062570631
 
< 0.1%
22.703388851
 
< 0.1%
34.189453061
 
< 0.1%
Other values (28160)28160
99.9%
ValueCountFrequency (%)
-3031
0.1%
-24.962949091
 
< 0.1%
-20.775812851
 
< 0.1%
-16.110305441
 
< 0.1%
-7.4736611881
 
< 0.1%
-6.8589977141
 
< 0.1%
-3.7549954351
 
< 0.1%
-0.57958745791
 
< 0.1%
-0.42583847791
 
< 0.1%
0.41974131281
 
< 0.1%
ValueCountFrequency (%)
551
< 0.1%
54.853127211
< 0.1%
54.709285631
< 0.1%
54.107139651
< 0.1%
53.908026561
< 0.1%
53.641727321
< 0.1%
53.271806961
< 0.1%
53.237897961
< 0.1%
53.163322521
< 0.1%
53.123298241
< 0.1%

windmill_body_temperature(°C)
Real number (ℝ)

MISSING

Distinct21893
Distinct (%)84.7%
Missing2363
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean20.79976057
Minimum-999
Maximum323
Zeros0
Zeros (%)0.0%
Negative4662
Negative (%)16.5%
Memory size440.6 KiB
2021-05-27T04:22:30.095325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-99
Q140.44838554
median42.78683182
Q344.49454294
95-th percentile48.45719436
Maximum323
Range1322
Interquartile range (IQR)4.046157401

Descriptive statistics

Standard deviation54.35643133
Coefficient of variation (CV)2.61332005
Kurtosis15.19760084
Mean20.79976057
Median Absolute Deviation (MAD)1.905431183
Skewness-2.236832172
Sum537403.4138
Variance2954.621626
MonotonicityNot monotonic
2021-05-27T04:22:30.246325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-993926
 
13.9%
3012
 
< 0.1%
-57
 
< 0.1%
-9993
 
< 0.1%
47.688574781
 
< 0.1%
-6.0222878331
 
< 0.1%
8.3617795751
 
< 0.1%
43.552943761
 
< 0.1%
46.998523471
 
< 0.1%
43.2492681
 
< 0.1%
Other values (21883)21883
77.6%
(Missing)2363
 
8.4%
ValueCountFrequency (%)
-9993
 
< 0.1%
-100.26887561
 
< 0.1%
-993926
13.9%
-72.265504841
 
< 0.1%
-70.808794411
 
< 0.1%
-68.952214281
 
< 0.1%
-67.836130111
 
< 0.1%
-67.634208111
 
< 0.1%
-67.548713091
 
< 0.1%
-64.734315881
 
< 0.1%
ValueCountFrequency (%)
3231
< 0.1%
160.2932711
< 0.1%
158.22371611
< 0.1%
152.6067521
< 0.1%
151.46737521
< 0.1%
151.17244191
< 0.1%
145.14597871
< 0.1%
143.01830291
< 0.1%
142.11496621
< 0.1%
141.05795661
< 0.1%

wind_direction(°)
Real number (ℝ≥0)

MISSING

Distinct22984
Distinct (%)99.5%
Missing5103
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean306.888883
Minimum0
Maximum569.9664788
Zeros114
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size440.6 KiB
2021-05-27T04:22:30.405325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.31054547
Q1238.6277523
median271.4276556
Q3404.1535172
95-th percentile532.6099781
Maximum569.9664788
Range569.9664788
Interquartile range (IQR)165.5257649

Descriptive statistics

Standard deviation134.0559003
Coefficient of variation (CV)0.4368222759
Kurtosis-0.3182066048
Mean306.888883
Median Absolute Deviation (MAD)47.43804242
Skewness0.1719416134
Sum7088212.53
Variance17970.98441
MonotonicityNot monotonic
2021-05-27T04:22:30.554325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0114
 
0.4%
290.81925311
 
< 0.1%
524.09085371
 
< 0.1%
535.71500961
 
< 0.1%
235.7858741
 
< 0.1%
327.91465021
 
< 0.1%
232.36794791
 
< 0.1%
489.0447531
 
< 0.1%
468.49501061
 
< 0.1%
290.25011511
 
< 0.1%
Other values (22974)22974
81.5%
(Missing)5103
 
18.1%
ValueCountFrequency (%)
0114
0.4%
0.7828325021
 
< 0.1%
0.91113147051
 
< 0.1%
1.7239588231
 
< 0.1%
1.7946284791
 
< 0.1%
1.8056854911
 
< 0.1%
1.9465485821
 
< 0.1%
2.0565085821
 
< 0.1%
2.1406292271
 
< 0.1%
2.1473771891
 
< 0.1%
ValueCountFrequency (%)
569.96647881
< 0.1%
569.8792361
< 0.1%
569.86829981
< 0.1%
569.29286451
< 0.1%
568.88892511
< 0.1%
568.86547761
< 0.1%
568.50489791
< 0.1%
568.28041651
< 0.1%
567.15023741
< 0.1%
567.05605631
< 0.1%

resistance(ohm)
Real number (ℝ)

Distinct27365
Distinct (%)97.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1575.560011
Minimum-1005.222988
Maximum4693.481933
Zeros0
Zeros (%)0.0%
Negative616
Negative (%)2.2%
Memory size440.6 KiB
2021-05-27T04:22:30.708325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1005.222988
5-th percentile750.3534087
Q11268.134043
median1678.238404
Q31829.054007
95-th percentile1991.922098
Maximum4693.481933
Range5698.704921
Interquartile range (IQR)560.9199639

Descriptive statistics

Standard deviation483.3263944
Coefficient of variation (CV)0.306764827
Kurtosis3.738642985
Mean1575.560011
Median Absolute Deviation (MAD)257.0085571
Skewness-0.6978086536
Sum44429216.76
Variance233604.4035
MonotonicityNot monotonic
2021-05-27T04:22:30.842324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99558
 
2.0%
1172.554732198
 
0.7%
1672.55473281
 
0.3%
1280.9148121
 
< 0.1%
1655.8100821
 
< 0.1%
1362.7996061
 
< 0.1%
1978.7440921
 
< 0.1%
1489.5263891
 
< 0.1%
1838.1035211
 
< 0.1%
1163.9085871
 
< 0.1%
Other values (27355)27355
97.0%
ValueCountFrequency (%)
-1005.2229881
< 0.1%
-825.15681821
< 0.1%
-821.59497491
< 0.1%
-814.4902381
< 0.1%
-781.67109981
< 0.1%
-763.21286891
< 0.1%
-717.96084871
< 0.1%
-630.75967941
< 0.1%
-630.14184011
< 0.1%
-565.89039011
< 0.1%
ValueCountFrequency (%)
4693.4819331
< 0.1%
4389.3650641
< 0.1%
4327.1174531
< 0.1%
4304.5259341
< 0.1%
4295.634071
< 0.1%
4187.0145841
< 0.1%
4150.2671591
< 0.1%
4131.6157611
< 0.1%
4120.6692491
< 0.1%
4050.292011
< 0.1%

rotor_torque(N-m)
Real number (ℝ)

MISSING

Distinct25945
Distinct (%)93.9%
Missing572
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean25.84989405
Minimum-136.7322169
Maximum236.8832642
Zeros0
Zeros (%)0.0%
Negative2437
Negative (%)8.6%
Memory size440.6 KiB
2021-05-27T04:22:30.993287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-136.7322169
5-th percentile-34.51517149
Q113.72280757
median32.97719178
Q341.55051976
95-th percentile75.51415065
Maximum236.8832642
Range373.6154811
Interquartile range (IQR)27.8277122

Descriptive statistics

Standard deviation32.42394292
Coefficient of variation (CV)1.254316279
Kurtosis5.190170929
Mean25.84989405
Median Absolute Deviation (MAD)13.61863893
Skewness-1.030947446
Sum714180.8728
Variance1051.312074
MonotonicityNot monotonic
2021-05-27T04:22:31.140288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99571
 
2.0%
20334
 
1.2%
21.14181606317
 
1.1%
5242
 
0.9%
5.570908032224
 
0.8%
34.207074621
 
< 0.1%
33.526169811
 
< 0.1%
41.172361541
 
< 0.1%
43.99908011
 
< 0.1%
34.508156921
 
< 0.1%
Other values (25935)25935
92.0%
(Missing)572
 
2.0%
ValueCountFrequency (%)
-136.73221691
< 0.1%
-134.90309921
< 0.1%
-134.34225911
< 0.1%
-130.77857321
< 0.1%
-129.91263081
< 0.1%
-127.22319931
< 0.1%
-124.50702821
< 0.1%
-120.91188821
< 0.1%
-120.60079841
< 0.1%
-119.21042641
< 0.1%
ValueCountFrequency (%)
236.88326421
< 0.1%
208.32017571
< 0.1%
192.62579841
< 0.1%
189.11178991
< 0.1%
187.15771211
< 0.1%
184.69395011
< 0.1%
176.53252791
< 0.1%
175.90052181
< 0.1%
174.58376451
< 0.1%
174.28774341
< 0.1%

turbine_status
Categorical

MISSING

Distinct14
Distinct (%)0.1%
Missing1759
Missing (%)6.2%
Memory size1.8 MiB
BB
1946 
AAA
1939 
BCB
1933 
B2
1931 
A
1930 
Other values (9)
16762 

Length

Max length3
Median length2
Mean length2.069059415
Min length1

Characters and Unicode

Total characters54708
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBA
2nd rowA2
3rd rowABC
4th rowABC
5th rowAAA
ValueCountFrequency (%)
BB1946
 
6.9%
AAA1939
 
6.9%
BCB1933
 
6.9%
B21931
 
6.8%
A1930
 
6.8%
D1922
 
6.8%
B1882
 
6.7%
AB1868
 
6.6%
ABC1867
 
6.6%
A21855
 
6.6%
Other values (4)7368
26.1%
2021-05-27T04:22:31.449287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bb1946
 
7.4%
aaa1939
 
7.3%
bcb1933
 
7.3%
b21931
 
7.3%
a1930
 
7.3%
d1922
 
7.3%
b1882
 
7.1%
ab1868
 
7.1%
abc1867
 
7.1%
a21855
 
7.0%
Other values (4)7368
27.9%

Most occurring characters

ValueCountFrequency (%)
B24466
44.7%
A17041
31.1%
C5650
 
10.3%
23786
 
6.9%
D3765
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter50922
93.1%
Decimal Number3786
 
6.9%

Most frequent character per category

ValueCountFrequency (%)
B24466
48.0%
A17041
33.5%
C5650
 
11.1%
D3765
 
7.4%
ValueCountFrequency (%)
23786
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin50922
93.1%
Common3786
 
6.9%

Most frequent character per script

ValueCountFrequency (%)
B24466
48.0%
A17041
33.5%
C5650
 
11.1%
D3765
 
7.4%
ValueCountFrequency (%)
23786
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII54708
100.0%

Most frequent character per block

ValueCountFrequency (%)
B24466
44.7%
A17041
31.1%
C5650
 
10.3%
23786
 
6.9%
D3765
 
6.9%

cloud_level
Categorical

Distinct3
Distinct (%)< 0.1%
Missing276
Missing (%)1.0%
Memory size1.9 MiB
Low
13921 
Medium
13704 
Extremely Low
 
299

Length

Max length13
Median length6
Mean length4.579358258
Min length3

Characters and Unicode

Total characters127874
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowMedium
5th rowLow
ValueCountFrequency (%)
Low13921
49.4%
Medium13704
48.6%
Extremely Low299
 
1.1%
(Missing)276
 
1.0%
2021-05-27T04:22:31.727308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-27T04:22:31.806287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
low14220
50.4%
medium13704
48.6%
extremely299
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e14302
11.2%
L14220
11.1%
o14220
11.1%
w14220
11.1%
m14003
11.0%
M13704
10.7%
d13704
10.7%
i13704
10.7%
u13704
10.7%
E299
 
0.2%
Other values (6)1794
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter99352
77.7%
Uppercase Letter28223
 
22.1%
Space Separator299
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
e14302
14.4%
o14220
14.3%
w14220
14.3%
m14003
14.1%
d13704
13.8%
i13704
13.8%
u13704
13.8%
x299
 
0.3%
t299
 
0.3%
r299
 
0.3%
Other values (2)598
 
0.6%
ValueCountFrequency (%)
L14220
50.4%
M13704
48.6%
E299
 
1.1%
ValueCountFrequency (%)
299
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin127575
99.8%
Common299
 
0.2%

Most frequent character per script

ValueCountFrequency (%)
e14302
11.2%
L14220
11.1%
o14220
11.1%
w14220
11.1%
m14003
11.0%
M13704
10.7%
d13704
10.7%
i13704
10.7%
u13704
10.7%
E299
 
0.2%
Other values (5)1495
 
1.2%
ValueCountFrequency (%)
299
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII127874
100.0%

Most frequent character per block

ValueCountFrequency (%)
e14302
11.2%
L14220
11.1%
o14220
11.1%
w14220
11.1%
m14003
11.0%
M13704
10.7%
d13704
10.7%
i13704
10.7%
u13704
10.7%
E299
 
0.2%
Other values (6)1794
 
1.4%

blade_length(m)
Real number (ℝ)

MISSING

Distinct22833
Distinct (%)98.8%
Missing5093
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean2.254034285
Minimum-99
Maximum18.20980014
Zeros0
Zeros (%)0.0%
Negative1441
Negative (%)5.1%
Memory size440.6 KiB
2021-05-27T04:22:31.918317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-0.5880672138
Q12.544858676
median3.453333244
Q34.357862443
95-th percentile6.937745799
Maximum18.20980014
Range117.2098001
Interquartile range (IQR)1.813003767

Descriptive statistics

Standard deviation11.27560208
Coefficient of variation (CV)5.002409305
Kurtosis74.42047689
Mean2.254034285
Median Absolute Deviation (MAD)0.9063817367
Skewness-8.60835754
Sum52083.97023
Variance127.1392024
MonotonicityNot monotonic
2021-05-27T04:22:32.069317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99275
 
1.0%
0.36057111541
 
< 0.1%
2.9342831671
 
< 0.1%
2.6222533591
 
< 0.1%
3.2511181481
 
< 0.1%
3.9257604331
 
< 0.1%
2.8566477481
 
< 0.1%
3.8437044251
 
< 0.1%
3.8717457381
 
< 0.1%
4.0710992841
 
< 0.1%
Other values (22823)22823
80.9%
(Missing)5093
 
18.1%
ValueCountFrequency (%)
-99275
1.0%
-8.2534778011
 
< 0.1%
-7.6246330731
 
< 0.1%
-7.5389486211
 
< 0.1%
-7.5362532361
 
< 0.1%
-7.2195810461
 
< 0.1%
-7.1920452581
 
< 0.1%
-7.1001714121
 
< 0.1%
-7.0916093111
 
< 0.1%
-6.9445129361
 
< 0.1%
ValueCountFrequency (%)
18.209800141
< 0.1%
16.392794751
< 0.1%
15.88866681
< 0.1%
15.691489251
< 0.1%
15.215189821
< 0.1%
14.919053581
< 0.1%
13.912993591
< 0.1%
13.908019171
< 0.1%
13.888556511
< 0.1%
13.835551321
< 0.1%

blade_breadth(m)
Real number (ℝ≥0)

UNIQUE

Distinct28200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3972487151
Minimum0.2001109965
Maximum0.4999752685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size440.6 KiB
2021-05-27T04:22:32.229330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.2001109965
5-th percentile0.3057614748
Q10.3474450578
median0.3985910349
Q30.4493544961
95-th percentile0.4898062667
Maximum0.4999752685
Range0.299864272
Interquartile range (IQR)0.1019094383

Descriptive statistics

Standard deviation0.06115832915
Coefficient of variation (CV)0.1539547563
Kurtosis-0.7409660378
Mean0.3972487151
Median Absolute Deviation (MAD)0.05099301237
Skewness-0.1934013189
Sum11202.41377
Variance0.003740341224
MonotonicityNot monotonic
2021-05-27T04:22:32.379330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.31980846241
 
< 0.1%
0.47605747841
 
< 0.1%
0.48894250291
 
< 0.1%
0.46864124981
 
< 0.1%
0.36990451441
 
< 0.1%
0.46434124141
 
< 0.1%
0.31378494491
 
< 0.1%
0.46835233981
 
< 0.1%
0.42903859441
 
< 0.1%
0.42642858291
 
< 0.1%
Other values (28190)28190
> 99.9%
ValueCountFrequency (%)
0.20011099651
< 0.1%
0.20067874521
< 0.1%
0.20080813341
< 0.1%
0.20162199721
< 0.1%
0.20162676281
< 0.1%
0.20169900171
< 0.1%
0.20185820541
< 0.1%
0.20247682851
< 0.1%
0.20256009191
< 0.1%
0.20275082791
< 0.1%
ValueCountFrequency (%)
0.49997526851
< 0.1%
0.49995631191
< 0.1%
0.49995539591
< 0.1%
0.49995523371
< 0.1%
0.49994375061
< 0.1%
0.49994140961
< 0.1%
0.49992862561
< 0.1%
0.49990932621
< 0.1%
0.49990891421
< 0.1%
0.4998921281
< 0.1%

windmill_height(m)
Real number (ℝ)

MISSING

Distinct27657
Distinct (%)100.0%
Missing543
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean25.88705208
Minimum-30.29525292
Maximum78.35133528
Zeros0
Zeros (%)0.0%
Negative149
Negative (%)0.5%
Memory size440.6 KiB
2021-05-27T04:22:32.538333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-30.29525292
5-th percentile11.58236226
Q124.4476578
median25.95773932
Q327.4778543
95-th percentile40.35738287
Maximum78.35133528
Range108.6465882
Interquartile range (IQR)3.030196501

Descriptive statistics

Standard deviation7.773608982
Coefficient of variation (CV)0.3002894636
Kurtosis4.514326981
Mean25.88705208
Median Absolute Deviation (MAD)1.514542357
Skewness-0.1132801349
Sum715958.1993
Variance60.4289966
MonotonicityNot monotonic
2021-05-27T04:22:32.685330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.202466711
 
< 0.1%
31.591468191
 
< 0.1%
43.757195721
 
< 0.1%
4.5192692491
 
< 0.1%
24.977853411
 
< 0.1%
21.544304531
 
< 0.1%
19.669071921
 
< 0.1%
24.503475741
 
< 0.1%
24.351050151
 
< 0.1%
26.18843241
 
< 0.1%
Other values (27647)27647
98.0%
(Missing)543
 
1.9%
ValueCountFrequency (%)
-30.295252921
< 0.1%
-20.918635751
< 0.1%
-19.334192691
< 0.1%
-18.43973371
< 0.1%
-18.216456451
< 0.1%
-17.480419451
< 0.1%
-17.102686261
< 0.1%
-17.032022041
< 0.1%
-16.30261991
< 0.1%
-16.210538621
< 0.1%
ValueCountFrequency (%)
78.351335281
< 0.1%
71.005204331
< 0.1%
69.924717721
< 0.1%
69.872902991
< 0.1%
68.955707531
< 0.1%
67.695362111
< 0.1%
67.044735691
< 0.1%
66.06229791
< 0.1%
65.719125991
< 0.1%
65.230243861
< 0.1%

windmill_generated_power(kW/h)
Real number (ℝ≥0)

Distinct27988
Distinct (%)> 99.9%
Missing207
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean6.130529296
Minimum0.9623049455
Maximum20.17535792
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size440.6 KiB
2021-05-27T04:22:32.836328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.9623049455
5-th percentile2.409117891
Q14.059505444
median5.764710347
Q37.94719459
95-th percentile10.91447972
Maximum20.17535792
Range19.21305297
Interquartile range (IQR)3.887689146

Descriptive statistics

Standard deviation2.697520359
Coefficient of variation (CV)0.4400142677
Kurtosis0.4370431408
Mean6.130529296
Median Absolute Deviation (MAD)1.913920779
Skewness0.6889352977
Sum171611.9066
Variance7.276616088
MonotonicityNot monotonic
2021-05-27T04:22:32.987168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.67182
 
< 0.1%
1.36169992
 
< 0.1%
3.0883735372
 
< 0.1%
4.51672
 
< 0.1%
2.81422
 
< 0.1%
2.160174881
 
< 0.1%
5.2007787321
 
< 0.1%
7.3843218671
 
< 0.1%
9.7050707141
 
< 0.1%
3.273759831
 
< 0.1%
Other values (27978)27978
99.2%
(Missing)207
 
0.7%
ValueCountFrequency (%)
0.96230494551
< 0.1%
0.98023995451
< 0.1%
1.0002443781
< 0.1%
1.007734941
< 0.1%
1.0282549361
< 0.1%
1.0304466031
< 0.1%
1.0459736291
< 0.1%
1.0507841351
< 0.1%
1.0632749131
< 0.1%
1.0668788771
< 0.1%
ValueCountFrequency (%)
20.175357921
< 0.1%
20.127939351
< 0.1%
19.99893951
< 0.1%
19.941388741
< 0.1%
19.312914051
< 0.1%
19.06340921
< 0.1%
18.983039251
< 0.1%
18.694224251
< 0.1%
18.61517851
< 0.1%
18.07581941
< 0.1%

Interactions

2021-05-27T04:21:37.785535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:37.955533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:38.122552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:38.292534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:38.433533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:38.566549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:38.725536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:38.898609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:39.041608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:39.172609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:39.312608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:39.445621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:39.580767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:39.725464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:39.856917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:39.998917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:40.135917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:40.300440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:40.488442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:40.653438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:40.807824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:40.960824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:41.103821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:41.279830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:41.444824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:41.604825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:41.894436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:42.099434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:42.260546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:42.419546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:42.621546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:42.785856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:42.939887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:43.084636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:43.261633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:43.399637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:43.542637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:43.680666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:43.820649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:43.951649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:44.086649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:44.227649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:44.385381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:44.553381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:44.694588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:44.827506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:44.961506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:45.105506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:45.249506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:45.391506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:45.524506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:45.672506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:45.812299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:45.970299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:46.110300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:46.254299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:46.390299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:46.529300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:46.671299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:46.814556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:46.944561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:47.088557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:47.262204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:47.583164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:21:47.754161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-27T04:22:17.708953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:17.880538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:18.021538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:18.173538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:18.317555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:18.457538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:18.607538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:18.738538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:18.880834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:19.012837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:19.148834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:19.293831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:19.428834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:19.561851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:19.698834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:19.826835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:19.949897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:20.090881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:20.227881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:20.370882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:20.521881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:20.664882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:20.808878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:20.959144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:21.108134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:21.271137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:21.420136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:21.577137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:21.734143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:21.881137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:22.047137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:22.248275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:22.401275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:22.545270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:22.701276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:22.855302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:23.006306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:23.178302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:23.336303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T04:22:23.494303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-27T04:22:33.153168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-27T04:22:33.504168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-27T04:22:33.851168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-27T04:22:34.625153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-27T04:22:34.962706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-27T04:22:23.817875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-27T04:22:24.501061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-27T04:22:25.131387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-27T04:22:25.968920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

tracking_iddatetimewind_speed(m/s)atmospheric_temperature(°C)shaft_temperature(°C)blades_angle(°)gearbox_temperature(°C)engine_temperature(°C)motor_torque(N-m)generator_temperature(°C)atmospheric_pressure(Pascal)area_temperature(°C)windmill_body_temperature(°C)wind_direction(°)resistance(ohm)rotor_torque(N-m)turbine_statuscloud_levelblade_length(m)blade_breadth(m)windmill_height(m)windmill_generated_power(kW/h)
0WM_337252019-08-04 14:33:2094.82-99.0041.72-0.9082.4142.522563.1276.67103402.9626.90nan239.842730.3142.08BAMedium2.220.3124.286.77
1WM_6982018-11-05 10:13:20241.8327.76-99.00-99.0044.1046.262372.3878.1317030.9039.80nan337.941780.21107.89A2Medium4.210.4527.265.97
2WM_391462019-09-14 14:03:2095.48nan41.8612.6542.3242.881657.1767.6516125.9336.1245.03227.851666.05-42.93ABCMedium2.720.3027.372.87
3WM_67572018-12-25 15:33:20238.82-99.0045.4415.1244.7647.282888.1395.3918689.7346.0244.83492.081964.5042.74ABCNaN4.860.3724.2914.85
4WM_215212019-05-04 03:13:2010.72nan41.981.72-17.6243.47781.7037.42114468.1734.57-99.00259.271177.5213.39AAAMediumnan0.4527.973.52
5WM_178732019-03-22 21:03:2093.7730.3317.97-99.0043.8240.822119.3572.35nan35.32101.38nan1715.2497.75ABCLow2.500.4024.674.95
6WM_198732019-04-17 18:33:2016.03-99.0044.07-0.2041.6843.38778.1140.28121813.3833.8543.01528.001222.9311.81BDLow2.920.4533.595.09
7WM_303302019-07-08 21:03:2048.7412.7243.22-99.00-48.4144.13980.9943.69120923.0230.55-99.00nan1177.6418.38BALow2.940.3529.948.54
8WM_260692019-06-07 17:53:2047.08-99.00-33.61-99.0043.0645.25957.5841.61119628.9626.1743.22281.37-99.0019.49ABCLow1.650.3046.738.74
9WM_289152019-06-28 16:13:20283.7918.8941.6952.34-62.7241.881042.0965.2816160.2829.38-99.00352.271662.0820.10ACExtremely Low1.060.2024.321.95

Last rows

tracking_iddatetimewind_speed(m/s)atmospheric_temperature(°C)shaft_temperature(°C)blades_angle(°)gearbox_temperature(°C)engine_temperature(°C)motor_torque(N-m)generator_temperature(°C)atmospheric_pressure(Pascal)area_temperature(°C)windmill_body_temperature(°C)wind_direction(°)resistance(ohm)rotor_torque(N-m)turbine_statuscloud_levelblade_length(m)blade_breadth(m)windmill_height(m)windmill_generated_power(kW/h)
28190WM_312292019-07-15 07:23:20-139.753.9019.27-1.2413.6415.80796.8539.07118873.5624.43nannan1265.2914.16AAALow4.360.4817.546.20
28191WM_77522019-01-01 16:23:2096.30-99.0048.107.6846.8445.222800.7897.8519156.6941.47-99.00513.11893.8547.96B2Mediumnan0.322.3510.39
28192WM_206102019-04-27 01:03:2020.187.0143.90-99.0043.2245.32845.4040.17120708.8731.2678.76273.861272.3815.57BBLow2.270.4825.376.45
28193WM_329132019-07-29 19:43:2095.0328.0545.41-1.1545.5743.222389.3075.3817735.5933.7844.54247.451776.52-99.00ABMedium2.420.4033.466.68
28194WM_214412019-05-02 21:53:2013.109.9442.0972.5139.9143.28757.1638.28116089.7037.792.41144.581196.7413.38BDMediumnan0.3524.294.19
28195WM_78142019-01-02 02:43:2094.7723.5845.405.38-1.0948.532791.6090.9019428.7345.4344.24536.151980.8645.91BBMedium2.770.4224.599.59
28196WM_325122019-07-26 12:53:2094.2024.0342.07-99.0044.2943.492207.8872.2416596.4925.1443.62354.241712.8436.97BBLow-3.250.4626.054.52
28197WM_51932018-12-12 02:13:2094.1628.6745.009.5549.3844.042801.6694.8119083.8845.1343.58534.211951.7388.32DMediumnan0.3828.5311.10
28198WM_121732019-02-03 19:13:2095.4326.5648.033.0581.4444.822760.6590.1418360.7945.6044.97568.501968.9247.56BCBLow3.000.3547.759.37
28199WM_330042019-07-30 11:43:2043.5618.729.222.5940.2640.902015.9869.04-256507.5522.4144.10320.501666.53-8.93BBBLownan0.3924.132.86